An intelligent reliability evaluation and service life prediction method for a kilometer deep well hoist brake, the method including: the establishment of a digital twin model for a hoist brake, data acquisition and synchronization, and reliability evaluation and service life prediction, wherein the digital twin model for the hoist brake can accurately reflect actual physical characteristics of the hoist brake, the data acquisition and synchronization can realize real-time mapping between a physical entity of the hoist brake and the digital twin model therefor, and furthermore, on the basis of the digital twin model for the hoist brake, the reliability evaluation and service life prediction are realized. Digital twin technology is combined with a reliability analysis method, so that real-time updating of reliability evaluation and service life prediction of the hoist brake are realized.
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2. The intelligent reliability evaluation and service life prediction method for the kilometer deep well hoist brake according to claim 1, wherein in Step G, after the obtaining of the reliability results corresponding to the braking performance parameters respectively, according to a correlation among the braking performance parameters, a copula function is applied to establish a system reliability model under a correlation of a multi-failure mode for the reliability results thereby obtaining the comprehensive reliability result corresponding to the target hoist brake.
4. The intelligent reliability evaluation and service life prediction method for the kilometer deep well hoist brake according to claim 3, wherein in Step III, after acquiring the service life prediction results corresponding to the second braking performance parameters related to the performance degradation, a time-varying copula function is applied to establish a life prediction model under a correlation of a multi-degradation process for each of the life prediction results, thereby obtaining a comprehensive life prediction result corresponding to the target hoist brake.
6. The intelligent reliability evaluation and service life prediction method for the kilometer deep well hoist brake according to claim 3, wherein the second braking performance parameters related to the performance degradation comprise a brake disc crack, a wear on the brake gate valve, and a stiffness of the belleville spring.
8. The intelligent reliability evaluation and service life prediction method for the kilometer deep well hoist brake according to claim 1, wherein in the Step A, after the obtaining of the first data and the second data and before the mapping of the first data and the second data into the digital twin model, the second data is filtered.
9. The intelligent reliability evaluation and service life prediction method for the kilometer deep well hoist brake according to claim 1, wherein the braking working condition parameters comprise lifting loads, initial braking speeds, braking times and braking forces, and the braking performance parameters comprise a temperature of the brake disc, a temperature of the brake gate valve, a deformation of the brake disc, a wear losses of the brake gate valve, and a stiffness of the be belleville spring.
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February 23, 2021
February 6, 2024
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